Generateur de nombres aleatoires

Random Number Generator

Generate one or more random integers within any range you define. This tool produces uniformly distributed results — every integer between your minimum and maximum has an equal probability of being selected. Use it for raffle drawings, random sampling, classroom exercises, game mechanics, statistical simulations, or any situation that calls for unbiased, instant random selection.

How Random Number Generation Is Calculated

This generator uses a pseudo-random algorithm seeded from system entropy. The probability formula is simple:

P(any specific number) = 1 / (Max - Min + 1)

For a range of 1 to 6, each number has a 1/6 = 16.67% chance. For 1 to 100, each number has a 1/100 = 1.00% chance. Each generation is statistically independent — prior results have no effect on the next one. This property (called independence) is what makes the tool fair for draws, raffles, and sampling.

Worked Examples

Scenario 1 — Classroom raffle with 35 students A teacher assigns each student a number from 1 to 35, then sets Min = 1, Max = 35, and generates 1 number. The winner is student number 22. Each student had a 1/35 = 2.86% chance. Regenerating picks a new winner with the same fairness.

Scenario 2 — Board game with two six-sided dice To simulate rolling 2d6, generate two numbers in the range 1-6 simultaneously. Sum them. The result ranges from 2 to 12. Note: generating a single number from 2 to 12 is not equivalent — it would give each value an equal 1/11 chance, but true 2d6 has 7 as the most probable result (6/36 = 16.67%) and 2 or 12 as least probable (1/36 = 2.78%).

Scenario 3 — Random sampling for a survey A researcher has 500 customer records and wants a random sample of 30. Number the records 1 to 500, generate 30 unique numbers in that range, and pull those records. This guarantees every record has a 30/500 = 6% chance of selection, satisfying simple random sampling requirements.

Range and Probability Reference Table

When to Use This Tool

• Running a giveaway or raffle and needing a transparent, unbiased winner selection
• Assigning participants to treatment and control groups for an A/B test or classroom experiment
• Generating test data with random IDs, amounts, or index values during software development
• Selecting a random item from a numbered list — a book from a reading pile, a recipe to cook, a team to play first
• Rolling dice or simulating chance events in tabletop games without physical dice on hand

Common Mistakes

1. Regenerating until you get a “better” number. Repeating generations to achieve a preferred outcome eliminates the randomness entirely and introduces bias. Accept the first result for any fair application.
2. Simulating 2d6 with a single range 2-12. As shown above, a single uniform draw from 2-12 does not replicate two-dice probability. Roll two separate 1-6 results and sum them.
3. Confusing range size with result fairness. A range of 1-3 is just as fair as 1-1,000,000 — each number within the given range has equal probability. “Fairness” comes from uniform distribution, not a large range.
4. Using this for cryptographic purposes. Browser-based pseudo-random generators are not suitable for password generation, token creation, or security keys. Use a dedicated cryptographic random source for those needs.

Context and Applications

Random number generators appear in a wide variety of real-world fields. In statistics and research, simple random sampling requires each population member to have an equal selection probability — exactly what a uniform RNG provides. In education, teachers use random draws to call on students, assign groups, or run fair lotteries for limited resources. In gaming, tabletop RPGs rely on random dice rolls (d4, d6, d8, d10, d12, d20, d100) to determine outcomes. All of these map directly to ranges this tool supports. In software testing, random integers are used to populate databases with realistic-looking IDs, generate edge-case inputs, and stress-test systems with unpredictable values.

Tips

1. For a coin flip, set Min = 1, Max = 2 and designate 1 as heads, 2 as tails
2. To pick from a numbered list, assign each item a sequential integer and generate a single number in that range
3. Generate multiple numbers at once for group assignments — e.g., generate 5 numbers from 1-30 to select five survey respondents
4. For RPG dice, use ranges 1-4 (d4), 1-8 (d8), 1-10 (d10), 1-12 (d12), and 1-100 (d100 or percentile)
5. Document your generation process for formal draws — record the range, number of entries, timestamp, and result for transparency
6. To get a random decimal between 0 and 1, generate a random integer from 0 to 10,000 and divide by 10,000